Miles Jamie, Jacques Richard, Turner Janette, Mason Suzanne
CURE Group, School of Health and Related Research, University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA, UK.
Yorkshire Ambulance Service, Brindley Way, Wakefield, WF2 0XQ, UK.
Diagn Progn Res. 2021 Nov 8;5(1):18. doi: 10.1186/s41512-021-00108-4.
Demand for both the ambulance service and the emergency department (ED) is rising every year and when this demand is excessive in both systems, ambulance crews queue at the ED waiting to hand patients over. Some transported ambulance patients are 'low-acuity' and do not require the treatment of the ED. However, paramedics can find it challenging to identify these patients accurately. Decision support tools have been developed using expert opinion to help identify these low acuity patients but have failed to show a benefit beyond regular decision-making. Predictive algorithms may be able to build accurate models, which can be used in the field to support the decision not to take a low-acuity patient to an ED.
All patients in Yorkshire who were transported to the ED by ambulance between July 2019 and February 2020 will be included. Ambulance electronic patient care record (ePCR) clinical data will be used as candidate predictors for the model. These will then be linked to the corresponding ED record, which holds the outcome of a 'non-urgent attendance'. The estimated sample size is 52,958, with 4767 events and an EPP of 7.48. An XGBoost algorithm will be used for model development. Initially, a model will be derived using all the data and the apparent performance will be assessed. Then internal-external validation will use non-random nested cross-validation (CV) with test sets held out for each ED (spatial validation). After all models are created, a random-effects meta-analysis will be undertaken. This will pool performance measures such as goodness of fit, discrimination and calibration. It will also generate a prediction interval and measure heterogeneity between clusters. The performance of the full model will be updated with the pooled results.
Creating a risk prediction model in this area will lead to further development of a clinical decision support tool that ensures every ambulance patient can get to the right place of care, first time. If this study is successful, it could help paramedics evaluate the benefit of transporting a patient to the ED before they leave the scene. It could also reduce congestion in the urgent and emergency care system.
This study was retrospectively registered with the ISRCTN: 12121281.
对救护车服务和急诊科（ED）的需求每年都在上升，当这两个系统的需求都过高时，救护人员会在急诊科排队等待移交患者。一些被转运的救护车患者是“低 acuity”患者，不需要急诊科的治疗。然而，护理人员可能会发现准确识别这些患者具有挑战性。已经利用专家意见开发了决策支持工具，以帮助识别这些低 acuity 患者，但未能显示出比常规决策更有优势。预测算法或许能够建立准确的模型，可在现场用于支持不将低 acuity 患者送往急诊科的决策。
将纳入 2019 年 7 月至 2020 年 2 月期间在约克郡由救护车转运至急诊科的所有患者。救护车电子患者护理记录（ePCR）临床数据将用作模型的候选预测指标。然后将这些数据与相应的急诊科记录相链接，该记录包含“非紧急就诊”的结果。估计样本量为 52958，有 4767 个事件，事件发生率为 7.48。将使用 XGBoost 算法进行模型开发。最初，将使用所有数据推导模型并评估其表面性能。然后，内部 - 外部验证将使用非随机嵌套交叉验证（CV），为每个急诊科留出测试集（空间验证）。创建所有模型后，将进行随机效应荟萃分析。这将汇总诸如拟合优度、区分度和校准等性能指标。它还将生成预测区间并测量各集群之间的异质性。完整模型的性能将根据汇总结果进行更新。
在该领域创建风险预测模型将促使临床决策支持工具进一步发展，确保每位救护车患者首次就能被送到合适的护理地点。如果这项研究成功，它可以帮助护理人员在离开现场前评估将患者送往急诊科的益处。它还可以减少紧急和急诊护理系统的拥堵。
本研究已在国际标准随机对照试验编号注册库（ISRCTN）进行回顾性注册：12121281。
